Overview

Dataset statistics

Number of variables31
Number of observations580322
Missing cells2848663
Missing cells (%)15.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory137.3 MiB
Average record size in memory248.0 B

Variable types

Numeric20
DateTime1
Categorical4
Text6

Alerts

CANCELLED is highly imbalanced (90.1%)Imbalance
DIVERTED is highly imbalanced (97.6%)Imbalance
DEP_TIME has 7150 (1.2%) missing valuesMissing
DEP_DELAY has 7152 (1.2%) missing valuesMissing
TAXI_OUT has 7319 (1.3%) missing valuesMissing
TAXI_IN has 7579 (1.3%) missing valuesMissing
ARR_TIME has 7579 (1.3%) missing valuesMissing
ARR_DELAY has 8789 (1.5%) missing valuesMissing
CANCELLATION_CODE has 572916 (98.7%) missing valuesMissing
AIR_TIME has 8789 (1.5%) missing valuesMissing
CARRIER_DELAY has 444278 (76.6%) missing valuesMissing
WEATHER_DELAY has 444278 (76.6%) missing valuesMissing
NAS_DELAY has 444278 (76.6%) missing valuesMissing
SECURITY_DELAY has 444278 (76.6%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 444278 (76.6%) missing valuesMissing
WEATHER_DELAY is highly skewed (γ1 = 22.8175781)Skewed
SECURITY_DELAY is highly skewed (γ1 = 54.90941663)Skewed
DEP_DELAY has 26867 (4.6%) zerosZeros
ARR_DELAY has 11025 (1.9%) zerosZeros
CARRIER_DELAY has 61079 (10.5%) zerosZeros
WEATHER_DELAY has 129948 (22.4%) zerosZeros
NAS_DELAY has 66192 (11.4%) zerosZeros
SECURITY_DELAY has 135274 (23.3%) zerosZeros
LATE_AIRCRAFT_DELAY has 67326 (11.6%) zerosZeros

Reproduction

Analysis started2024-03-30 06:21:13.634467
Analysis finished2024-03-30 06:24:51.993935
Duration3 minutes and 38.36 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.977926
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:24:52.714524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9153291
Coefficient of variation (CV)0.48148937
Kurtosis-1.1007519
Mean3.977926
Median Absolute Deviation (MAD)2
Skewness0.0091388446
Sum2308478
Variance3.6684856
MonotonicityIncreasing
2024-03-30T03:24:53.427408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 97407
16.8%
4 97144
16.7%
3 92208
15.9%
1 77761
13.4%
7 75302
13.0%
2 72908
12.6%
6 67592
11.6%
ValueCountFrequency (%)
1 77761
13.4%
2 72908
12.6%
3 92208
15.9%
4 97144
16.7%
5 97407
16.8%
6 67592
11.6%
7 75302
13.0%
ValueCountFrequency (%)
7 75302
13.0%
6 67592
11.6%
5 97407
16.8%
4 97144
16.7%
3 92208
15.9%
2 72908
12.6%
1 77761
13.4%
Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
Minimum2023-03-01 00:00:00
Maximum2023-03-31 00:00:00
2024-03-30T03:24:54.492593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:55.127391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
WN
117997 
DL
82791 
AA
78705 
UA
61590 
OO
57835 
Other values (10)
181404 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1160644
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 117997
20.3%
DL 82791
14.3%
AA 78705
13.6%
UA 61590
10.6%
OO 57835
10.0%
YX 27886
 
4.8%
B6 25793
 
4.4%
NK 22613
 
3.9%
AS 19975
 
3.4%
MQ 19380
 
3.3%
Other values (5) 65757
11.3%

Length

2024-03-30T03:24:55.455108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 117997
20.3%
dl 82791
14.3%
aa 78705
13.6%
ua 61590
10.6%
oo 57835
10.0%
yx 27886
 
4.8%
b6 25793
 
4.4%
nk 22613
 
3.9%
as 19975
 
3.4%
mq 19380
 
3.3%
Other values (5) 65757
11.3%

Most occurring characters

ValueCountFrequency (%)
A 245789
21.2%
N 140610
12.1%
O 132141
11.4%
W 117997
10.2%
D 82791
 
7.1%
L 82791
 
7.1%
U 61590
 
5.3%
9 31402
 
2.7%
Y 27886
 
2.4%
X 27886
 
2.4%
Other values (11) 209761
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1160644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 245789
21.2%
N 140610
12.1%
O 132141
11.4%
W 117997
10.2%
D 82791
 
7.1%
L 82791
 
7.1%
U 61590
 
5.3%
9 31402
 
2.7%
Y 27886
 
2.4%
X 27886
 
2.4%
Other values (11) 209761
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1160644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 245789
21.2%
N 140610
12.1%
O 132141
11.4%
W 117997
10.2%
D 82791
 
7.1%
L 82791
 
7.1%
U 61590
 
5.3%
9 31402
 
2.7%
Y 27886
 
2.4%
X 27886
 
2.4%
Other values (11) 209761
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1160644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 245789
21.2%
N 140610
12.1%
O 132141
11.4%
W 117997
10.2%
D 82791
 
7.1%
L 82791
 
7.1%
U 61590
 
5.3%
9 31402
 
2.7%
Y 27886
 
2.4%
X 27886
 
2.4%
Other values (11) 209761
18.1%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5860
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2261.6067
Minimum1
Maximum8819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:24:55.777064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile265
Q11010
median2017
Q33282
95-th percentile5334
Maximum8819
Range8818
Interquartile range (IQR)2272

Descriptive statistics

Standard deviation1559.435
Coefficient of variation (CV)0.68952527
Kurtosis-0.5414145
Mean2261.6067
Median Absolute Deviation (MAD)1080
Skewness0.63613163
Sum1.3124601 × 109
Variance2431837.4
MonotonicityNot monotonic
2024-03-30T03:24:56.240174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
517 319
 
0.1%
334 311
 
0.1%
321 307
 
0.1%
1103 291
 
0.1%
1633 291
 
0.1%
680 290
 
< 0.1%
522 288
 
< 0.1%
2086 286
 
< 0.1%
1378 285
 
< 0.1%
1318 285
 
< 0.1%
Other values (5850) 577369
99.5%
ValueCountFrequency (%)
1 144
< 0.1%
2 185
< 0.1%
3 192
< 0.1%
4 179
< 0.1%
5 94
< 0.1%
6 98
< 0.1%
7 114
< 0.1%
8 109
< 0.1%
9 179
< 0.1%
10 208
< 0.1%
ValueCountFrequency (%)
8819 1
 
< 0.1%
8801 1
 
< 0.1%
8792 1
 
< 0.1%
8791 1
 
< 0.1%
8790 2
< 0.1%
8789 1
 
< 0.1%
8788 4
< 0.1%
8787 1
 
< 0.1%
8786 1
 
< 0.1%
8785 1
 
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct338
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12650.406
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:24:56.663171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1525.4316
Coefficient of variation (CV)0.12058361
Kurtosis-1.2897586
Mean12650.406
Median Absolute Deviation (MAD)1591
Skewness0.10792407
Sum7.3413087 × 109
Variance2326941.5
MonotonicityNot monotonic
2024-03-30T03:24:57.059782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 28350
 
4.9%
11292 23694
 
4.1%
11298 22583
 
3.9%
13930 21905
 
3.8%
11057 16457
 
2.8%
12892 15910
 
2.7%
12889 15890
 
2.7%
14107 15557
 
2.7%
12953 14763
 
2.5%
13204 14593
 
2.5%
Other values (328) 390620
67.3%
ValueCountFrequency (%)
10135 392
 
0.1%
10136 84
 
< 0.1%
10140 1704
0.3%
10141 62
 
< 0.1%
10146 89
 
< 0.1%
10155 84
 
< 0.1%
10157 149
 
< 0.1%
10158 248
 
< 0.1%
10165 9
 
< 0.1%
10170 58
 
< 0.1%
ValueCountFrequency (%)
16869 147
 
< 0.1%
16218 124
 
< 0.1%
15991 62
 
< 0.1%
15919 943
0.2%
15841 62
 
< 0.1%
15624 699
0.1%
15607 92
 
< 0.1%
15582 54
 
< 0.1%
15569 54
 
< 0.1%
15412 1125
0.2%

ORIGIN
Text

Distinct338
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:24:57.928883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1740966
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLGA
2nd rowDTW
3rd rowGRB
4th rowATL
5th rowFAY
ValueCountFrequency (%)
atl 28350
 
4.9%
den 23694
 
4.1%
dfw 22583
 
3.9%
ord 21905
 
3.8%
clt 16457
 
2.8%
lax 15910
 
2.7%
las 15890
 
2.7%
phx 15557
 
2.7%
lga 14763
 
2.5%
mco 14593
 
2.5%
Other values (328) 390620
67.3%
2024-03-30T03:24:59.520615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 197706
 
11.4%
L 161157
 
9.3%
S 148393
 
8.5%
D 134795
 
7.7%
T 92346
 
5.3%
O 89007
 
5.1%
C 88035
 
5.1%
M 77487
 
4.5%
F 71488
 
4.1%
P 67712
 
3.9%
Other values (16) 612840
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1740966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 197706
 
11.4%
L 161157
 
9.3%
S 148393
 
8.5%
D 134795
 
7.7%
T 92346
 
5.3%
O 89007
 
5.1%
C 88035
 
5.1%
M 77487
 
4.5%
F 71488
 
4.1%
P 67712
 
3.9%
Other values (16) 612840
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1740966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 197706
 
11.4%
L 161157
 
9.3%
S 148393
 
8.5%
D 134795
 
7.7%
T 92346
 
5.3%
O 89007
 
5.1%
C 88035
 
5.1%
M 77487
 
4.5%
F 71488
 
4.1%
P 67712
 
3.9%
Other values (16) 612840
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1740966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 197706
 
11.4%
L 161157
 
9.3%
S 148393
 
8.5%
D 134795
 
7.7%
T 92346
 
5.3%
O 89007
 
5.1%
C 88035
 
5.1%
M 77487
 
4.5%
F 71488
 
4.1%
P 67712
 
3.9%
Other values (16) 612840
35.2%
Distinct332
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:00.248757image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.03145
Min length8

Characters and Unicode

Total characters7562437
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York, NY
2nd rowDetroit, MI
3rd rowGreen Bay, WI
4th rowAtlanta, GA
5th rowFayetteville, NC
ValueCountFrequency (%)
ca 60865
 
4.5%
tx 58449
 
4.3%
fl 56562
 
4.2%
ny 33978
 
2.5%
new 31368
 
2.3%
ga 30547
 
2.3%
il 30167
 
2.2%
san 29060
 
2.1%
chicago 28976
 
2.1%
atlanta 28350
 
2.1%
Other values (403) 966125
71.3%
2024-03-30T03:25:02.754329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
774125
 
10.2%
, 580322
 
7.7%
a 576157
 
7.6%
o 416846
 
5.5%
e 400611
 
5.3%
n 370287
 
4.9%
t 358811
 
4.7%
l 326780
 
4.3%
i 284236
 
3.8%
r 276401
 
3.7%
Other values (46) 3197861
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7562437
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
774125
 
10.2%
, 580322
 
7.7%
a 576157
 
7.6%
o 416846
 
5.5%
e 400611
 
5.3%
n 370287
 
4.9%
t 358811
 
4.7%
l 326780
 
4.3%
i 284236
 
3.8%
r 276401
 
3.7%
Other values (46) 3197861
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7562437
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
774125
 
10.2%
, 580322
 
7.7%
a 576157
 
7.6%
o 416846
 
5.5%
e 400611
 
5.3%
n 370287
 
4.9%
t 358811
 
4.7%
l 326780
 
4.3%
i 284236
 
3.8%
r 276401
 
3.7%
Other values (46) 3197861
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7562437
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
774125
 
10.2%
, 580322
 
7.7%
a 576157
 
7.6%
o 416846
 
5.5%
e 400611
 
5.3%
n 370287
 
4.9%
t 358811
 
4.7%
l 326780
 
4.3%
i 284236
 
3.8%
r 276401
 
3.7%
Other values (46) 3197861
42.3%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:03.755879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1463205
Min length4

Characters and Unicode

Total characters4727489
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowMichigan
3rd rowWisconsin
4th rowGeorgia
5th rowNorth Carolina
ValueCountFrequency (%)
california 60865
 
9.1%
texas 58449
 
8.8%
florida 56562
 
8.5%
new 49475
 
7.4%
york 33978
 
5.1%
georgia 30547
 
4.6%
illinois 30167
 
4.5%
carolina 28628
 
4.3%
colorado 27600
 
4.1%
north 25302
 
3.8%
Other values (51) 265511
39.8%
2024-03-30T03:25:05.848202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 629408
13.3%
i 532018
 
11.3%
o 457105
 
9.7%
r 348102
 
7.4%
n 342829
 
7.3%
e 289987
 
6.1%
s 265632
 
5.6%
l 263525
 
5.6%
d 119717
 
2.5%
C 119187
 
2.5%
Other values (37) 1359979
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4727489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 629408
13.3%
i 532018
 
11.3%
o 457105
 
9.7%
r 348102
 
7.4%
n 342829
 
7.3%
e 289987
 
6.1%
s 265632
 
5.6%
l 263525
 
5.6%
d 119717
 
2.5%
C 119187
 
2.5%
Other values (37) 1359979
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4727489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 629408
13.3%
i 532018
 
11.3%
o 457105
 
9.7%
r 348102
 
7.4%
n 342829
 
7.3%
e 289987
 
6.1%
s 265632
 
5.6%
l 263525
 
5.6%
d 119717
 
2.5%
C 119187
 
2.5%
Other values (37) 1359979
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4727489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 629408
13.3%
i 532018
 
11.3%
o 457105
 
9.7%
r 348102
 
7.4%
n 342829
 
7.3%
e 289987
 
6.1%
s 265632
 
5.6%
l 263525
 
5.6%
d 119717
 
2.5%
C 119187
 
2.5%
Other values (37) 1359979
28.8%

ORIGIN_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.013518
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:06.806676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q133
median44
Q381
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.75784
Coefficient of variation (CV)0.49539153
Kurtosis-1.3253331
Mean54.013518
Median Absolute Deviation (MAD)22
Skewness0.0068371993
Sum31345233
Variance715.98198
MonotonicityNot monotonic
2024-03-30T03:25:07.469704image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 60865
 
10.5%
74 58449
 
10.1%
33 56562
 
9.7%
22 33978
 
5.9%
34 30547
 
5.3%
41 30167
 
5.2%
82 27600
 
4.8%
36 23842
 
4.1%
38 20314
 
3.5%
81 18061
 
3.1%
Other values (42) 219937
37.9%
ValueCountFrequency (%)
1 2730
 
0.5%
2 11396
2.0%
3 2906
 
0.5%
4 609
 
0.1%
5 103
 
< 0.1%
11 2094
 
0.4%
12 1001
 
0.2%
13 12490
2.2%
14 548
 
0.1%
15 1262
 
0.2%
ValueCountFrequency (%)
93 14849
 
2.6%
92 6313
 
1.1%
91 60865
10.5%
88 1039
 
0.2%
87 9873
 
1.7%
86 1921
 
0.3%
85 17534
 
3.0%
84 2106
 
0.4%
83 2374
 
0.4%
82 27600
4.8%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct338
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12650.369
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:08.211691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1525.3828
Coefficient of variation (CV)0.1205801
Kurtosis-1.2897138
Mean12650.369
Median Absolute Deviation (MAD)1591
Skewness0.1080226
Sum7.3412873 × 109
Variance2326792.6
MonotonicityNot monotonic
2024-03-30T03:25:08.979179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 28339
 
4.9%
11292 23699
 
4.1%
11298 22582
 
3.9%
13930 21909
 
3.8%
11057 16451
 
2.8%
12892 15913
 
2.7%
12889 15895
 
2.7%
14107 15546
 
2.7%
12953 14764
 
2.5%
13204 14593
 
2.5%
Other values (328) 390631
67.3%
ValueCountFrequency (%)
10135 392
 
0.1%
10136 84
 
< 0.1%
10140 1702
0.3%
10141 62
 
< 0.1%
10146 89
 
< 0.1%
10155 84
 
< 0.1%
10157 148
 
< 0.1%
10158 248
 
< 0.1%
10165 9
 
< 0.1%
10170 58
 
< 0.1%
ValueCountFrequency (%)
16869 146
 
< 0.1%
16218 124
 
< 0.1%
15991 62
 
< 0.1%
15919 944
0.2%
15841 62
 
< 0.1%
15624 701
0.1%
15607 92
 
< 0.1%
15582 56
 
< 0.1%
15569 54
 
< 0.1%
15412 1124
0.2%

DEST
Text

Distinct338
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:10.486328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1740966
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBGM
2nd rowGRB
3rd rowDTW
4th rowFAY
5th rowATL
ValueCountFrequency (%)
atl 28339
 
4.9%
den 23699
 
4.1%
dfw 22582
 
3.9%
ord 21909
 
3.8%
clt 16451
 
2.8%
lax 15913
 
2.7%
las 15895
 
2.7%
phx 15546
 
2.7%
lga 14764
 
2.5%
mco 14593
 
2.5%
Other values (328) 390631
67.3%
2024-03-30T03:25:12.812569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 197701
 
11.4%
L 161164
 
9.3%
S 148398
 
8.5%
D 134803
 
7.7%
T 92318
 
5.3%
O 88998
 
5.1%
C 88015
 
5.1%
M 77477
 
4.5%
F 71499
 
4.1%
P 67690
 
3.9%
Other values (16) 612903
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1740966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 197701
 
11.4%
L 161164
 
9.3%
S 148398
 
8.5%
D 134803
 
7.7%
T 92318
 
5.3%
O 88998
 
5.1%
C 88015
 
5.1%
M 77477
 
4.5%
F 71499
 
4.1%
P 67690
 
3.9%
Other values (16) 612903
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1740966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 197701
 
11.4%
L 161164
 
9.3%
S 148398
 
8.5%
D 134803
 
7.7%
T 92318
 
5.3%
O 88998
 
5.1%
C 88015
 
5.1%
M 77477
 
4.5%
F 71499
 
4.1%
P 67690
 
3.9%
Other values (16) 612903
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1740966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 197701
 
11.4%
L 161164
 
9.3%
S 148398
 
8.5%
D 134803
 
7.7%
T 92318
 
5.3%
O 88998
 
5.1%
C 88015
 
5.1%
M 77477
 
4.5%
F 71499
 
4.1%
P 67690
 
3.9%
Other values (16) 612903
35.2%
Distinct332
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:14.989093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.031553
Min length8

Characters and Unicode

Total characters7562497
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBinghamton, NY
2nd rowGreen Bay, WI
3rd rowDetroit, MI
4th rowFayetteville, NC
5th rowAtlanta, GA
ValueCountFrequency (%)
ca 60848
 
4.5%
tx 58444
 
4.3%
fl 56565
 
4.2%
ny 33991
 
2.5%
new 31374
 
2.3%
ga 30537
 
2.3%
il 30169
 
2.2%
san 29059
 
2.1%
chicago 28977
 
2.1%
atlanta 28339
 
2.1%
Other values (403) 966163
71.3%
2024-03-30T03:25:18.536289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
774144
 
10.2%
, 580322
 
7.7%
a 576128
 
7.6%
o 416865
 
5.5%
e 400663
 
5.3%
n 370259
 
4.9%
t 358789
 
4.7%
l 326782
 
4.3%
i 284233
 
3.8%
r 276413
 
3.7%
Other values (46) 3197899
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7562497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
774144
 
10.2%
, 580322
 
7.7%
a 576128
 
7.6%
o 416865
 
5.5%
e 400663
 
5.3%
n 370259
 
4.9%
t 358789
 
4.7%
l 326782
 
4.3%
i 284233
 
3.8%
r 276413
 
3.7%
Other values (46) 3197899
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7562497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
774144
 
10.2%
, 580322
 
7.7%
a 576128
 
7.6%
o 416865
 
5.5%
e 400663
 
5.3%
n 370259
 
4.9%
t 358789
 
4.7%
l 326782
 
4.3%
i 284233
 
3.8%
r 276413
 
3.7%
Other values (46) 3197899
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7562497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
774144
 
10.2%
, 580322
 
7.7%
a 576128
 
7.6%
o 416865
 
5.5%
e 400663
 
5.3%
n 370259
 
4.9%
t 358789
 
4.7%
l 326782
 
4.3%
i 284233
 
3.8%
r 276413
 
3.7%
Other values (46) 3197899
42.3%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:19.294485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1462705
Min length4

Characters and Unicode

Total characters4727460
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowWisconsin
3rd rowMichigan
4th rowNorth Carolina
5th rowGeorgia
ValueCountFrequency (%)
california 60848
 
9.1%
texas 58444
 
8.8%
florida 56565
 
8.5%
new 49491
 
7.4%
york 33991
 
5.1%
georgia 30537
 
4.6%
illinois 30169
 
4.5%
carolina 28631
 
4.3%
colorado 27602
 
4.1%
north 25297
 
3.8%
Other values (51) 265529
39.8%
2024-03-30T03:25:20.541569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 629391
13.3%
i 531968
 
11.3%
o 457083
 
9.7%
r 348092
 
7.4%
n 342813
 
7.3%
e 290009
 
6.1%
s 265619
 
5.6%
l 263525
 
5.6%
d 119737
 
2.5%
C 119175
 
2.5%
Other values (37) 1360048
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4727460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 629391
13.3%
i 531968
 
11.3%
o 457083
 
9.7%
r 348092
 
7.4%
n 342813
 
7.3%
e 290009
 
6.1%
s 265619
 
5.6%
l 263525
 
5.6%
d 119737
 
2.5%
C 119175
 
2.5%
Other values (37) 1360048
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4727460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 629391
13.3%
i 531968
 
11.3%
o 457083
 
9.7%
r 348092
 
7.4%
n 342813
 
7.3%
e 290009
 
6.1%
s 265619
 
5.6%
l 263525
 
5.6%
d 119737
 
2.5%
C 119175
 
2.5%
Other values (37) 1360048
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4727460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 629391
13.3%
i 531968
 
11.3%
o 457083
 
9.7%
r 348092
 
7.4%
n 342813
 
7.3%
e 290009
 
6.1%
s 265619
 
5.6%
l 263525
 
5.6%
d 119737
 
2.5%
C 119175
 
2.5%
Other values (37) 1360048
28.8%

DEST_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.009879
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:21.108915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q133
median44
Q381
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.758489
Coefficient of variation (CV)0.49543694
Kurtosis-1.3252958
Mean54.009879
Median Absolute Deviation (MAD)22
Skewness0.0069820568
Sum31343121
Variance716.01675
MonotonicityNot monotonic
2024-03-30T03:25:21.774179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 60848
 
10.5%
74 58444
 
10.1%
33 56565
 
9.7%
22 33991
 
5.9%
34 30537
 
5.3%
41 30169
 
5.2%
82 27602
 
4.8%
36 23839
 
4.1%
38 20319
 
3.5%
81 18052
 
3.1%
Other values (42) 219956
37.9%
ValueCountFrequency (%)
1 2729
 
0.5%
2 11402
2.0%
3 2909
 
0.5%
4 611
 
0.1%
5 102
 
< 0.1%
11 2094
 
0.4%
12 1001
 
0.2%
13 12491
2.2%
14 549
 
0.1%
15 1263
 
0.2%
ValueCountFrequency (%)
93 14850
 
2.6%
92 6311
 
1.1%
91 60848
10.5%
88 1038
 
0.2%
87 9877
 
1.7%
86 1919
 
0.3%
85 17539
 
3.0%
84 2106
 
0.4%
83 2375
 
0.4%
82 27602
4.8%

CRS_DEP_TIME
Real number (ℝ)

Distinct1231
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1336.3206
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:22.470560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile600
Q1910
median1325
Q31745
95-th percentile2135
Maximum2359
Range2358
Interquartile range (IQR)835

Descriptive statistics

Standard deviation499.12958
Coefficient of variation (CV)0.37351036
Kurtosis-1.0837561
Mean1336.3206
Median Absolute Deviation (MAD)419
Skewness0.074943314
Sum7.7549623 × 108
Variance249130.34
MonotonicityNot monotonic
2024-03-30T03:25:24.364873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 13752
 
2.4%
700 9270
 
1.6%
800 5743
 
1.0%
900 4231
 
0.7%
730 3420
 
0.6%
630 3356
 
0.6%
1000 3302
 
0.6%
615 3233
 
0.6%
1100 3053
 
0.5%
1300 3034
 
0.5%
Other values (1221) 527928
91.0%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 1
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
5 3
 
< 0.1%
6 4
 
< 0.1%
7 2
 
< 0.1%
9 6
 
< 0.1%
10 15
< 0.1%
12 5
 
< 0.1%
ValueCountFrequency (%)
2359 979
0.2%
2357 16
 
< 0.1%
2356 25
 
< 0.1%
2355 189
 
< 0.1%
2354 61
 
< 0.1%
2353 20
 
< 0.1%
2352 11
 
< 0.1%
2351 26
 
< 0.1%
2350 274
 
< 0.1%
2349 31
 
< 0.1%

DEP_TIME
Real number (ℝ)

MISSING 

Distinct1416
Distinct (%)0.2%
Missing7150
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1339.3518
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:24.831863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile557
Q1911
median1330
Q31754
95-th percentile2151
Maximum2400
Range2399
Interquartile range (IQR)843

Descriptive statistics

Standard deviation515.20328
Coefficient of variation (CV)0.38466613
Kurtosis-1.0009205
Mean1339.3518
Median Absolute Deviation (MAD)422
Skewness0.019532348
Sum7.6767897 × 108
Variance265434.42
MonotonicityNot monotonic
2024-03-30T03:25:25.806094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1698
 
0.3%
556 1563
 
0.3%
557 1432
 
0.2%
554 1431
 
0.2%
558 1364
 
0.2%
559 1244
 
0.2%
600 1207
 
0.2%
553 1193
 
0.2%
655 1188
 
0.2%
657 1076
 
0.2%
Other values (1406) 559776
96.5%
(Missing) 7150
 
1.2%
ValueCountFrequency (%)
1 90
< 0.1%
2 65
< 0.1%
3 65
< 0.1%
4 61
< 0.1%
5 67
< 0.1%
6 72
< 0.1%
7 60
< 0.1%
8 57
< 0.1%
9 54
< 0.1%
10 49
< 0.1%
ValueCountFrequency (%)
2400 65
< 0.1%
2359 90
< 0.1%
2358 96
< 0.1%
2357 87
< 0.1%
2356 109
< 0.1%
2355 112
< 0.1%
2354 104
< 0.1%
2353 120
< 0.1%
2352 133
< 0.1%
2351 106
< 0.1%

DEP_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1150
Distinct (%)0.2%
Missing7152
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean13.197591
Minimum-42
Maximum4413
Zeros26867
Zeros (%)4.6%
Negative313590
Negative (%)54.0%
Memory size4.4 MiB
2024-03-30T03:25:26.815181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-42
5-th percentile-10
Q1-5
median-1
Q312
95-th percentile81
Maximum4413
Range4455
Interquartile range (IQR)17

Descriptive statistics

Standard deviation54.542107
Coefficient of variation (CV)4.1327321
Kurtosis333.49832
Mean13.197591
Median Absolute Deviation (MAD)6
Skewness12.707971
Sum7564463
Variance2974.8414
MonotonicityNot monotonic
2024-03-30T03:25:27.604769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 41106
 
7.1%
-4 38528
 
6.6%
-3 37803
 
6.5%
-2 34989
 
6.0%
-6 32343
 
5.6%
-1 31178
 
5.4%
-7 26890
 
4.6%
0 26867
 
4.6%
-8 21105
 
3.6%
-9 15847
 
2.7%
Other values (1140) 266514
45.9%
ValueCountFrequency (%)
-42 1
 
< 0.1%
-39 1
 
< 0.1%
-38 2
 
< 0.1%
-37 1
 
< 0.1%
-36 2
 
< 0.1%
-34 4
< 0.1%
-33 5
< 0.1%
-32 1
 
< 0.1%
-31 4
< 0.1%
-30 5
< 0.1%
ValueCountFrequency (%)
4413 1
< 0.1%
3249 1
< 0.1%
2810 1
< 0.1%
2681 1
< 0.1%
2552 1
< 0.1%
2368 1
< 0.1%
2255 1
< 0.1%
2111 1
< 0.1%
2019 1
< 0.1%
1996 1
< 0.1%

TAXI_OUT
Real number (ℝ)

MISSING 

Distinct167
Distinct (%)< 0.1%
Missing7319
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean17.853163
Minimum1
Maximum201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:28.161151image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median15
Q321
95-th percentile35
Maximum201
Range200
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.6376635
Coefficient of variation (CV)0.53982947
Kurtosis18.328562
Mean17.853163
Median Absolute Deviation (MAD)4
Skewness3.058238
Sum10229916
Variance92.884558
MonotonicityNot monotonic
2024-03-30T03:25:29.035972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 44883
 
7.7%
13 44779
 
7.7%
14 42638
 
7.3%
11 41324
 
7.1%
15 38300
 
6.6%
16 34385
 
5.9%
10 33816
 
5.8%
17 29732
 
5.1%
18 25862
 
4.5%
9 24258
 
4.2%
Other values (157) 213026
36.7%
ValueCountFrequency (%)
1 18
 
< 0.1%
2 19
 
< 0.1%
3 58
 
< 0.1%
4 182
 
< 0.1%
5 588
 
0.1%
6 2372
 
0.4%
7 6882
 
1.2%
8 14135
2.4%
9 24258
4.2%
10 33816
5.8%
ValueCountFrequency (%)
201 1
 
< 0.1%
181 1
 
< 0.1%
180 1
 
< 0.1%
169 3
< 0.1%
168 1
 
< 0.1%
165 2
< 0.1%
163 1
 
< 0.1%
160 1
 
< 0.1%
159 1
 
< 0.1%
158 2
< 0.1%

TAXI_IN
Real number (ℝ)

MISSING 

Distinct140
Distinct (%)< 0.1%
Missing7579
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean7.954889
Minimum1
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:29.674770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile18
Maximum160
Range159
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.1259727
Coefficient of variation (CV)0.77008903
Kurtosis37.578995
Mean7.954889
Median Absolute Deviation (MAD)2
Skewness4.236224
Sum4556107
Variance37.527542
MonotonicityNot monotonic
2024-03-30T03:25:30.325064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 85122
14.7%
5 81039
14.0%
6 67578
11.6%
7 55528
9.6%
3 52369
9.0%
8 42693
7.4%
9 34109
 
5.9%
10 26935
 
4.6%
11 20864
 
3.6%
12 16559
 
2.9%
Other values (130) 89947
15.5%
ValueCountFrequency (%)
1 790
 
0.1%
2 12968
 
2.2%
3 52369
9.0%
4 85122
14.7%
5 81039
14.0%
6 67578
11.6%
7 55528
9.6%
8 42693
7.4%
9 34109
5.9%
10 26935
 
4.6%
ValueCountFrequency (%)
160 1
 
< 0.1%
158 1
 
< 0.1%
156 2
< 0.1%
154 1
 
< 0.1%
149 1
 
< 0.1%
146 1
 
< 0.1%
144 1
 
< 0.1%
142 2
< 0.1%
141 2
< 0.1%
139 3
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

Distinct1335
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1488.109
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:30.985490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile714
Q11100
median1518
Q31930
95-th percentile2302
Maximum2359
Range2358
Interquartile range (IQR)830

Descriptive statistics

Standard deviation531.02286
Coefficient of variation (CV)0.35684405
Kurtosis-0.45276381
Mean1488.109
Median Absolute Deviation (MAD)416
Skewness-0.31467084
Sum8.635824 × 108
Variance281985.27
MonotonicityNot monotonic
2024-03-30T03:25:31.368362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 3000
 
0.5%
1640 1717
 
0.3%
1810 1699
 
0.3%
1900 1679
 
0.3%
2355 1623
 
0.3%
1915 1614
 
0.3%
1845 1591
 
0.3%
935 1583
 
0.3%
930 1581
 
0.3%
2120 1565
 
0.3%
Other values (1325) 562670
97.0%
ValueCountFrequency (%)
1 87
 
< 0.1%
2 66
 
< 0.1%
3 113
 
< 0.1%
4 172
 
< 0.1%
5 736
0.1%
6 125
 
< 0.1%
7 139
 
< 0.1%
8 108
 
< 0.1%
9 107
 
< 0.1%
10 504
0.1%
ValueCountFrequency (%)
2359 3000
0.5%
2358 917
 
0.2%
2357 933
 
0.2%
2356 654
 
0.1%
2355 1623
0.3%
2354 618
 
0.1%
2353 357
 
0.1%
2352 387
 
0.1%
2351 391
 
0.1%
2350 823
 
0.1%

ARR_TIME
Real number (ℝ)

MISSING 

Distinct1440
Distinct (%)0.3%
Missing7579
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean1456.8325
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:32.328610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile613
Q11040
median1502
Q31922
95-th percentile2257
Maximum2400
Range2399
Interquartile range (IQR)882

Descriptive statistics

Standard deviation557.90234
Coefficient of variation (CV)0.38295572
Kurtosis-0.35157085
Mean1456.8325
Median Absolute Deviation (MAD)440
Skewness-0.40488674
Sum8.3439062 × 108
Variance311255.02
MonotonicityNot monotonic
2024-03-30T03:25:32.957632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
946 635
 
0.1%
1851 628
 
0.1%
1640 622
 
0.1%
2139 618
 
0.1%
1114 617
 
0.1%
937 617
 
0.1%
1840 613
 
0.1%
938 612
 
0.1%
1620 612
 
0.1%
1906 612
 
0.1%
Other values (1430) 566557
97.6%
(Missing) 7579
 
1.3%
ValueCountFrequency (%)
1 467
0.1%
2 398
0.1%
3 375
0.1%
4 375
0.1%
5 368
0.1%
6 357
0.1%
7 369
0.1%
8 323
0.1%
9 315
0.1%
10 336
0.1%
ValueCountFrequency (%)
2400 367
0.1%
2359 417
0.1%
2358 405
0.1%
2357 450
0.1%
2356 409
0.1%
2355 467
0.1%
2354 443
0.1%
2353 456
0.1%
2352 483
0.1%
2351 469
0.1%

ARR_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1167
Distinct (%)0.2%
Missing8789
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean9.0699067
Minimum-80
Maximum4405
Zeros11025
Zeros (%)1.9%
Negative321212
Negative (%)55.4%
Memory size4.4 MiB
2024-03-30T03:25:33.745688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-80
5-th percentile-25
Q1-13
median-3
Q313
95-th percentile82
Maximum4405
Range4485
Interquartile range (IQR)26

Descriptive statistics

Standard deviation56.296564
Coefficient of variation (CV)6.2069618
Kurtosis288.93795
Mean9.0699067
Median Absolute Deviation (MAD)12
Skewness11.467622
Sum5183751
Variance3169.3031
MonotonicityNot monotonic
2024-03-30T03:25:34.129906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9 15137
 
2.6%
-11 15136
 
2.6%
-10 14945
 
2.6%
-12 14800
 
2.6%
-8 14799
 
2.6%
-7 14519
 
2.5%
-6 14349
 
2.5%
-13 14170
 
2.4%
-14 13793
 
2.4%
-5 13525
 
2.3%
Other values (1157) 426360
73.5%
ValueCountFrequency (%)
-80 1
 
< 0.1%
-78 1
 
< 0.1%
-77 1
 
< 0.1%
-75 1
 
< 0.1%
-74 1
 
< 0.1%
-73 2
 
< 0.1%
-71 2
 
< 0.1%
-69 2
 
< 0.1%
-68 3
< 0.1%
-67 5
< 0.1%
ValueCountFrequency (%)
4405 1
< 0.1%
3246 1
< 0.1%
2807 1
< 0.1%
2663 1
< 0.1%
2559 1
< 0.1%
2271 1
< 0.1%
2098 1
< 0.1%
2016 1
< 0.1%
2015 1
< 0.1%
1949 1
< 0.1%

CANCELLED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0.0
572916 
1.0
 
7406

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1740966
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 572916
98.7%
1.0 7406
 
1.3%

Length

2024-03-30T03:25:34.452789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:25:34.697940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 572916
98.7%
1.0 7406
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1153238
66.2%
. 580322
33.3%
1 7406
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1740966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1153238
66.2%
. 580322
33.3%
1 7406
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1740966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1153238
66.2%
. 580322
33.3%
1 7406
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1740966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1153238
66.2%
. 580322
33.3%
1 7406
 
0.4%

CANCELLATION_CODE
Categorical

MISSING 

Distinct4
Distinct (%)0.1%
Missing572916
Missing (%)98.7%
Memory size4.4 MiB
B
4444 
A
2444 
C
513 
D
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7406
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 4444
 
0.8%
A 2444
 
0.4%
C 513
 
0.1%
D 5
 
< 0.1%
(Missing) 572916
98.7%

Length

2024-03-30T03:25:35.084457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:25:35.350521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
b 4444
60.0%
a 2444
33.0%
c 513
 
6.9%
d 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
B 4444
60.0%
A 2444
33.0%
C 513
 
6.9%
D 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 4444
60.0%
A 2444
33.0%
C 513
 
6.9%
D 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 4444
60.0%
A 2444
33.0%
C 513
 
6.9%
D 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 4444
60.0%
A 2444
33.0%
C 513
 
6.9%
D 5
 
0.1%

DIVERTED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0.0
578939 
1.0
 
1383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1740966
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 578939
99.8%
1.0 1383
 
0.2%

Length

2024-03-30T03:25:35.869055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:25:36.283766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 578939
99.8%
1.0 1383
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1159261
66.6%
. 580322
33.3%
1 1383
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1740966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1159261
66.6%
. 580322
33.3%
1 1383
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1740966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1159261
66.6%
. 580322
33.3%
1 1383
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1740966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1159261
66.6%
. 580322
33.3%
1 1383
 
0.1%

AIR_TIME
Real number (ℝ)

MISSING 

Distinct645
Distinct (%)0.1%
Missing8789
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean116.22568
Minimum8
Maximum724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:36.815112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile36
Q164
median100
Q3146
95-th percentile269
Maximum724
Range716
Interquartile range (IQR)82

Descriptive statistics

Standard deviation72.047693
Coefficient of variation (CV)0.61989479
Kurtosis3.0451039
Mean116.22568
Median Absolute Deviation (MAD)40
Skewness1.5007338
Sum66426811
Variance5190.87
MonotonicityNot monotonic
2024-03-30T03:25:37.223579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 4816
 
0.8%
61 4787
 
0.8%
62 4778
 
0.8%
60 4688
 
0.8%
59 4671
 
0.8%
64 4657
 
0.8%
57 4652
 
0.8%
63 4602
 
0.8%
45 4592
 
0.8%
65 4542
 
0.8%
Other values (635) 524748
90.4%
(Missing) 8789
 
1.5%
ValueCountFrequency (%)
8 5
 
< 0.1%
9 14
 
< 0.1%
10 19
 
< 0.1%
11 7
 
< 0.1%
12 3
 
< 0.1%
13 9
 
< 0.1%
14 16
 
< 0.1%
15 33
 
< 0.1%
16 59
< 0.1%
17 132
< 0.1%
ValueCountFrequency (%)
724 1
< 0.1%
701 1
< 0.1%
695 1
< 0.1%
692 1
< 0.1%
686 1
< 0.1%
684 2
< 0.1%
683 1
< 0.1%
676 1
< 0.1%
675 1
< 0.1%
674 2
< 0.1%

CARRIER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct889
Distinct (%)0.7%
Missing444278
Missing (%)76.6%
Infinite0
Infinite (%)0.0%
Mean22.428847
Minimum0
Maximum3957
Zeros61079
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:37.782178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q321
95-th percentile91
Maximum3957
Range3957
Interquartile range (IQR)21

Descriptive statistics

Standard deviation71.493927
Coefficient of variation (CV)3.1875882
Kurtosis264.43183
Mean22.428847
Median Absolute Deviation (MAD)3
Skewness12.266369
Sum3051310
Variance5111.3816
MonotonicityNot monotonic
2024-03-30T03:25:38.447107image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61079
 
10.5%
1 2596
 
0.4%
2 2575
 
0.4%
3 2461
 
0.4%
6 2437
 
0.4%
4 2372
 
0.4%
5 2338
 
0.4%
7 2337
 
0.4%
15 2297
 
0.4%
8 2195
 
0.4%
Other values (879) 53357
 
9.2%
(Missing) 444278
76.6%
ValueCountFrequency (%)
0 61079
10.5%
1 2596
 
0.4%
2 2575
 
0.4%
3 2461
 
0.4%
4 2372
 
0.4%
5 2338
 
0.4%
6 2437
 
0.4%
7 2337
 
0.4%
8 2195
 
0.4%
9 2017
 
0.3%
ValueCountFrequency (%)
3957 1
< 0.1%
3246 1
< 0.1%
2807 1
< 0.1%
2015 1
< 0.1%
1996 1
< 0.1%
1949 1
< 0.1%
1928 1
< 0.1%
1861 1
< 0.1%
1853 1
< 0.1%
1776 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct444
Distinct (%)0.3%
Missing444278
Missing (%)76.6%
Infinite0
Infinite (%)0.0%
Mean3.0412146
Minimum0
Maximum1738
Zeros129948
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:39.426305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1738
Range1738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.354942
Coefficient of variation (CV)9.6523746
Kurtosis729.03203
Mean3.0412146
Median Absolute Deviation (MAD)0
Skewness22.817578
Sum413739
Variance861.71265
MonotonicityNot monotonic
2024-03-30T03:25:40.471824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 129948
 
22.4%
3 141
 
< 0.1%
6 141
 
< 0.1%
15 133
 
< 0.1%
8 131
 
< 0.1%
2 127
 
< 0.1%
4 124
 
< 0.1%
7 124
 
< 0.1%
9 121
 
< 0.1%
10 121
 
< 0.1%
Other values (434) 4933
 
0.9%
(Missing) 444278
76.6%
ValueCountFrequency (%)
0 129948
22.4%
1 101
 
< 0.1%
2 127
 
< 0.1%
3 141
 
< 0.1%
4 124
 
< 0.1%
5 108
 
< 0.1%
6 141
 
< 0.1%
7 124
 
< 0.1%
8 131
 
< 0.1%
9 121
 
< 0.1%
ValueCountFrequency (%)
1738 1
< 0.1%
1728 1
< 0.1%
1214 1
< 0.1%
1207 1
< 0.1%
1201 1
< 0.1%
1169 1
< 0.1%
1113 1
< 0.1%
1106 1
< 0.1%
1105 1
< 0.1%
1091 1
< 0.1%

NAS_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct378
Distinct (%)0.3%
Missing444278
Missing (%)76.6%
Infinite0
Infinite (%)0.0%
Mean13.393351
Minimum0
Maximum1198
Zeros66192
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:41.210621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q318
95-th percentile56
Maximum1198
Range1198
Interquartile range (IQR)18

Descriptive statistics

Standard deviation29.167101
Coefficient of variation (CV)2.1777299
Kurtosis197.742
Mean13.393351
Median Absolute Deviation (MAD)1
Skewness9.0962065
Sum1822085
Variance850.71977
MonotonicityNot monotonic
2024-03-30T03:25:42.593079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66192
 
11.4%
1 3528
 
0.6%
15 2712
 
0.5%
2 2592
 
0.4%
16 2590
 
0.4%
3 2415
 
0.4%
17 2349
 
0.4%
4 2328
 
0.4%
5 2181
 
0.4%
18 2084
 
0.4%
Other values (368) 47073
 
8.1%
(Missing) 444278
76.6%
ValueCountFrequency (%)
0 66192
11.4%
1 3528
 
0.6%
2 2592
 
0.4%
3 2415
 
0.4%
4 2328
 
0.4%
5 2181
 
0.4%
6 1968
 
0.3%
7 1986
 
0.3%
8 1932
 
0.3%
9 1683
 
0.3%
ValueCountFrequency (%)
1198 1
< 0.1%
1094 1
< 0.1%
1054 1
< 0.1%
1015 1
< 0.1%
995 1
< 0.1%
994 1
< 0.1%
979 1
< 0.1%
948 2
< 0.1%
924 1
< 0.1%
911 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct96
Distinct (%)0.1%
Missing444278
Missing (%)76.6%
Infinite0
Infinite (%)0.0%
Mean0.13552968
Minimum0
Maximum376
Zeros135274
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:43.037119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum376
Range376
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.954861
Coefficient of variation (CV)21.802316
Kurtosis4812.3571
Mean0.13552968
Median Absolute Deviation (MAD)0
Skewness54.909417
Sum18438
Variance8.7312034
MonotonicityNot monotonic
2024-03-30T03:25:44.053896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135274
 
23.3%
15 39
 
< 0.1%
10 34
 
< 0.1%
7 32
 
< 0.1%
5 29
 
< 0.1%
17 29
 
< 0.1%
18 27
 
< 0.1%
16 25
 
< 0.1%
19 25
 
< 0.1%
6 24
 
< 0.1%
Other values (86) 506
 
0.1%
(Missing) 444278
76.6%
ValueCountFrequency (%)
0 135274
23.3%
1 22
 
< 0.1%
2 24
 
< 0.1%
3 17
 
< 0.1%
4 17
 
< 0.1%
5 29
 
< 0.1%
6 24
 
< 0.1%
7 32
 
< 0.1%
8 24
 
< 0.1%
9 18
 
< 0.1%
ValueCountFrequency (%)
376 1
< 0.1%
323 1
< 0.1%
301 1
< 0.1%
231 1
< 0.1%
201 1
< 0.1%
200 1
< 0.1%
157 1
< 0.1%
155 1
< 0.1%
152 1
< 0.1%
150 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct660
Distinct (%)0.5%
Missing444278
Missing (%)76.6%
Infinite0
Infinite (%)0.0%
Mean25.472869
Minimum0
Maximum2530
Zeros67326
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2024-03-30T03:25:44.555582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q330
95-th percentile111
Maximum2530
Range2530
Interquartile range (IQR)30

Descriptive statistics

Standard deviation57.694096
Coefficient of variation (CV)2.2649234
Kurtosis171.43697
Mean25.472869
Median Absolute Deviation (MAD)1
Skewness9.0226545
Sum3465431
Variance3328.6088
MonotonicityNot monotonic
2024-03-30T03:25:44.999656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67326
 
11.6%
15 1796
 
0.3%
16 1724
 
0.3%
17 1589
 
0.3%
18 1518
 
0.3%
19 1408
 
0.2%
20 1373
 
0.2%
21 1296
 
0.2%
14 1266
 
0.2%
13 1236
 
0.2%
Other values (650) 55512
 
9.6%
(Missing) 444278
76.6%
ValueCountFrequency (%)
0 67326
11.6%
1 850
 
0.1%
2 965
 
0.2%
3 866
 
0.1%
4 952
 
0.2%
5 962
 
0.2%
6 1089
 
0.2%
7 1060
 
0.2%
8 1118
 
0.2%
9 1071
 
0.2%
ValueCountFrequency (%)
2530 1
< 0.1%
2246 1
< 0.1%
2098 1
< 0.1%
1865 1
< 0.1%
1742 1
< 0.1%
1639 1
< 0.1%
1514 1
< 0.1%
1495 1
< 0.1%
1484 1
< 0.1%
1440 1
< 0.1%

Interactions

2024-03-30T03:24:26.634669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:21:46.919390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:00.259585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:07.553198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:16.926701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:24.332177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:33.498798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:41.933809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:49.585078image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:56.827423image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:03.567798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:10.591897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:18.058878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:24.656036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:31.877587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:39.779254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:47.438946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:54.525346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:00.610681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:06.267695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:26.969473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:21:48.865790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:00.585794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:08.444088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:17.278107image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:24.896883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:33.863615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:42.408030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:49.970508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:57.144174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:04.071025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:10.910855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:18.435388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:25.010754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:32.329664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:40.180343image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:47.746529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:54.847094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:00.890440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:06.630283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:27.234470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:21:49.357838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:00.958259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:09.601221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:17.649408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:25.341197image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:34.241013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:42.783858image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:50.407114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:57.416455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:04.401325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:11.231628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:18.758024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:23:33.143652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:40.557547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:48.075813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:55.151648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:01.124563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:06.998279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:27.621764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:21:49.884251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:01.358876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:10.181049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:18.048042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:25.815450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:34.605747image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:43.239632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:50.851937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:57.793879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:04.740576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:11.559641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:19.097232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:25.705764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:33.697935image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:40.973567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:48.400456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:55.474469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:01.394622image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:07.405327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:22:26.206043image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:34.924465image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:43.637958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:51.255118image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:58.172710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:05.073312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:11.884475image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:19.444730image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:26.090621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:34.074416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:41.309865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:48.681562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:55.732320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:01.764179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:07.845326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:28.655124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:21:51.059831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:02.133562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:11.049973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:18.788453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:26.622156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:35.740646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:44.085960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:51.681577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:58.553156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:05.426746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:12.276325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:19.794227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:26.541425image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:34.609072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:41.789679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:49.016598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:56.034111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:02.088498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:08.317822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:29.074519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:21:51.505670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:02.489153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:22:58.905612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:23:12.581738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:23:34.984681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:21:51.985359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:22:11.782816image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:22:44.828876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:52.418083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:23:27.358514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:23:56.646201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:22:37.559448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:22:48.624672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:56.274137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:02.951389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:09.939232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:17.373253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:24.025004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:31.055842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:39.120058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:46.846852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:53.779419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:59.804117image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:05.708488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:25.600305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:34.958222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:21:59.827272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:07.162356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:16.502105image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:23.893441image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:33.091916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:41.515795image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:49.038230image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:22:56.508872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:03.218429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:10.239823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:17.652951image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:24.307331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:31.376674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:39.422437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:47.169161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:23:54.124696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:00.151281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:05.989435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:24:26.032734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-30T03:24:36.107094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T03:24:39.704409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
013/6/2023 12:00:00 AM9E462912953LGANew York, NYNew York2210577BGMBinghamton, NYNew York2221052100.0-5.015.05.022112152.0-19.00.0NaN0.032.0NaNNaNNaNNaNNaN
113/6/2023 12:00:00 AM9E463011433DTWDetroit, MIMichigan4311977GRBGreen Bay, WIWisconsin451000956.0-4.09.02.010481006.0-42.00.0NaN0.059.0NaNNaNNaNNaNNaN
213/6/2023 12:00:00 AM9E463011977GRBGreen Bay, WIWisconsin4511433DTWDetroit, MIMichigan4311331153.020.010.08.013521356.04.00.0NaN0.045.0NaNNaNNaNNaNNaN
313/6/2023 12:00:00 AM9E463110397ATLAtlanta, GAGeorgia3411641FAYFayetteville, NCNorth Carolina3613551345.0-10.024.03.015141504.0-10.00.0NaN0.052.0NaNNaNNaNNaNNaN
413/6/2023 12:00:00 AM9E463111641FAYFayetteville, NCNorth Carolina3610397ATLAtlanta, GAGeorgia3416001604.04.012.06.017391722.0-17.00.0NaN0.060.0NaNNaNNaNNaNNaN
513/6/2023 12:00:00 AM9E463212478JFKNew York, NYNew York2212397ITHIthaca/Cortland, NYNew York2222002151.0-9.018.03.023122253.0-19.00.0NaN0.041.0NaNNaNNaNNaNNaN
613/6/2023 12:00:00 AM9E463311042CLECleveland, OHOhio4412953LGANew York, NYNew York2210151010.0-5.024.07.011561141.0-15.00.0NaN0.060.0NaNNaNNaNNaNNaN
713/6/2023 12:00:00 AM9E463312953LGANew York, NYNew York2211042CLECleveland, OHOhio44730725.0-5.020.06.0923911.0-12.00.0NaN0.080.0NaNNaNNaNNaNNaN
813/6/2023 12:00:00 AM9E463411193CVGCincinnati, OHKentucky5211433DTWDetroit, MIMichigan43620610.0-10.011.06.0741706.0-35.00.0NaN0.039.0NaNNaNNaNNaNNaN
913/6/2023 12:00:00 AM9E463510821BWIBaltimore, MDMaryland3512478JFKNew York, NYNew York2217021657.0-5.029.013.018451819.0-26.00.0NaN0.040.0NaNNaNNaNNaNNaN
DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
58031273/26/2023 12:00:00 AMYX585110721BOSBoston, MAMassachusetts1312339INDIndianapolis, INIndiana42920933.013.023.06.012061225.019.00.0NaN0.0143.00.00.019.00.00.0
58031373/26/2023 12:00:00 AMYX585210721BOSBoston, MAMassachusetts1314100PHLPhiladelphia, PAPennsylvania2319451944.0-1.021.04.021242115.0-9.00.0NaN0.066.0NaNNaNNaNNaNNaN
58031473/26/2023 12:00:00 AMYX585312478JFKNew York, NYNew York2210721BOSBoston, MAMassachusetts13600605.05.021.013.0717720.03.00.0NaN0.041.0NaNNaNNaNNaNNaN
58031573/26/2023 12:00:00 AMYX585412953LGANew York, NYNew York2210693BNANashville, TNTennessee5410501046.0-4.012.09.012381226.0-12.00.0NaN0.0139.0NaNNaNNaNNaNNaN
58031673/26/2023 12:00:00 AMYX585614524RICRichmond, VAVirginia3810721BOSBoston, MAMassachusetts1312401234.0-6.011.08.014221401.0-21.00.0NaN0.068.0NaNNaNNaNNaNNaN
58031773/26/2023 12:00:00 AMYX585712339INDIndianapolis, INIndiana4212953LGANew York, NYNew York2213551353.0-2.021.017.015561557.01.00.0NaN0.086.0NaNNaNNaNNaNNaN
58031873/26/2023 12:00:00 AMYX585810721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia3812201223.03.017.03.014081405.0-3.00.0NaN0.082.0NaNNaNNaNNaNNaN
58031973/26/2023 12:00:00 AMYX585912953LGANew York, NYNew York2215016STLSt. Louis, MOMissouri6414291423.0-6.017.06.016261622.0-4.00.0NaN0.0156.0NaNNaNNaNNaNNaN
58032073/26/2023 12:00:00 AMYX586110721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia3810151317.0182.019.05.012041500.0176.00.0NaN0.079.0176.00.00.00.00.0
58032173/26/2023 12:00:00 AMYX586212953LGANew York, NYNew York2214307PVDProvidence, RIRhode Island1516001617.017.013.05.017081705.0-3.00.0NaN0.030.0NaNNaNNaNNaNNaN